IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v111y2017i3d10.1007_s11192-017-2339-5.html
   My bibliography  Save this article

Discovering shifts in competitive strategies in probiotics, accelerated with TechMining

Author

Listed:
  • Jose M. Vicente-Gomila

    (Universitat Politècnica de València)

  • Anna Palli

    (IRTA Institut de Recerca Agroalimentária)

  • Begoña Calle

    (IRTA Institut de Recerca Agroalimentária)

  • Miguel A. Artacho

    (Universitat Politècnica de València)

  • Sara Jimenez

    (IRTA Institut de Recerca Agroalimentária)

Abstract

Profiling the technological strategy of different competitors is a key element for the companies in a given industry, as well to technology planners and R&D strategists. The analysis of the patent portfolio of a company as well as its evolution in the time line is of interest for technology analysts and decision makers. However, the need for the participation of experts in the field of a company as well as patent specialists, slows down the process. Bibliometrics and text mining techniques contribute to the interpretation of specialists. The present paper tries to offer a step by step procedure to analyze the technology strategy of several companies through the analysis of their portfolio claims, combined with the use of TechMining with the help of a text mining tool. The procedure, complemented with a semantic TRIZ analysis provides key insights in disclosing the technological analysis of some competitors in the field of probiotics for livestock health. The results show interesting shifts in the key probiotic and prebiotic ingredients for which companies claim protection and therefore offers clues about their technology intention in the life sciences industry in a more dynamic, convenient and simple way.

Suggested Citation

  • Jose M. Vicente-Gomila & Anna Palli & Begoña Calle & Miguel A. Artacho & Sara Jimenez, 2017. "Discovering shifts in competitive strategies in probiotics, accelerated with TechMining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1907-1923, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2339-5
    DOI: 10.1007/s11192-017-2339-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2339-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-017-2339-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    2. David J. TEECE, 2008. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," World Scientific Book Chapters, in: The Transfer And Licensing Of Know-How And Intellectual Property Understanding the Multinational Enterprise in the Modern World, chapter 5, pages 67-87, World Scientific Publishing Co. Pte. Ltd..
    3. Kim, Bongsun & Kim, Eonsoo & Miller, Douglas J. & Mahoney, Joseph T., 2016. "The impact of the timing of patents on innovation performance," Research Policy, Elsevier, vol. 45(4), pages 914-928.
    4. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    5. Tong, Xuesong & Frame, J. Davidson, 1994. "Measuring national technological performance with patent claims data," Research Policy, Elsevier, vol. 23(2), pages 133-141, March.
    6. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    7. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2012. "Identifying patent infringement using SAO based semantic technological similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 515-529, February.
    8. Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    9. Jean O. Lanjouw & Mark Schankerman, 1999. "The Quality of Ideas: Measuring Innovation with Multiple Indicators," NBER Working Papers 7345, National Bureau of Economic Research, Inc.
    10. Niwa, Sumiko, 2016. "Patent claims and economic growth," Economic Modelling, Elsevier, vol. 54(C), pages 377-381.
    11. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    2. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    2. Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
    3. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    4. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    5. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.
    6. Nicolas van Zeebroeck & Bruno van Pottelsberghe de la Potterie, 2011. "Filing strategies and patent value," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(6), pages 539-561, February.
    7. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    8. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    9. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    10. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    11. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    12. Appio, Francesco Paolo & Baglieri, Daniela & Cesaroni, Fabrizio & Spicuzza, Lucia & Donato, Alessia, 2022. "Patent design strategies: Empirical evidence from European patents," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    13. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    14. van Zeebroeck, Nicolas & van Pottelsberghe de la Potterie, Bruno & Guellec, Dominique, 2009. "Claiming more: the Increased Voluminosity of Patent Applications and its Determinants," Research Policy, Elsevier, vol. 38(6), pages 1006-1020, July.
    15. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2022. "Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations," Papers 2208.07222, arXiv.org.
    16. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    17. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2017. "Academic knowledge quality differentials and the quality of firm innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(5), pages 821-844.
    18. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    19. de Rassenfosse, Gaetan & van Pottelsberghe de la Potterie, Bruno, 2009. "A policy insight into the R&D-patent relationship," Research Policy, Elsevier, vol. 38(5), pages 779-792, June.
    20. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.

    More about this item

    Keywords

    Technology strategy; Bibliometrics; Animal health; Tech mining; Semantic TRIZ;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2339-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.